2024 StateofAIReport2024

• (Benaich & Chalmers, 2024) ⇒ Nathan Benaich, and Alex Chalmers. (2024). “State of AI Report 2024.”

Subject Headings: Frontier LLM.

Notes

1. Model Performance Convergence and Differentiation**: The performance gap between frontier models from Large Language Models (LLMs) like OpenAI, Meta, and Anthropic has significantly diminished, leading to a commoditization of model capabilities. This trend is shifting the competitive focus from raw performance to unique features and specialized use cases.
2. Multimodal Model Evolution and Cross-Domain Integration**: Foundation models are expanding into Multimodal AI by integrating text, images, and video, and exploring biological domains to enable new scientific and industrial applications. This evolution positions AI to become a cross-domain solution, capable of addressing complex problems in fields like robotics, healthcare, and mathematics.
3. AI Safety Research and AI Risk Mitigation**: The introduction of dedicated AI safety research sections aims to mitigate the risks posed by future AGI systems. New methodologies are emerging to address model vulnerabilities, adversarial attacks, and ensuring safe deployment of advanced AI capabilities.
4. Geopolitical AI Impact and Strategic AI Competition**: US sanctions on Chinese labs have prompted alternative strategies to develop competitive models despite restricted access to key technologies. This geopolitical competition highlights the need for sovereign AI strategies to secure domestic AI capabilities and influence the global AI landscape.
5. AI Hardware Market Dynamics and Vendor Strategy Evolution**: The GPU market is dominated by NVIDIA’s products, such as the Blackwell B200 and GB200 Superchip, with startups like Cerebras and Groq struggling to gain traction. The concentrated hardware landscape places immense power in the hands of a few vendors.
6. AI Regulation and AI Governance Frameworks**: Regional regulatory developments in the EU and the US are gaining momentum, while global governance remains in the voluntary stage. The regulatory uncertainty could hinder AI innovation and complicate compliance for companies operating in multiple regions.
7. Open-Source AI Models vs. Proprietary AI Models**: Open-source models like Llama 3 have gained community support and regulatory attention, creating a dynamic tension with closed models such as o1. This competition influences collaborative research and shapes innovation strategies in the field.
8. Economic Viability of AI Companies and AI Business Models**: Despite a surge in enterprise value to $9 trillion, only a few AI companies are achieving reliable revenue growth from AI-first offerings. The rapid evolution of the AI market raises concerns about long-term profitability and sustainability.
9. AI in Scientific Research and AI for Biological Applications**: AI is transforming scientific research through breakthroughs in protein folding and drug discovery, with models like AlphaFold 3 setting new benchmarks. These developments could revolutionize genomics and biotechnology by enabling more accurate predictions and accelerating scientific breakthroughs.
10. AI Agentic Behavior and AI Planning Capabilities**: New research into agentic behavior and advanced planning capabilities aims to equip autonomous systems to perform complex real-world interactions. By integrating reinforcement learning and self-improvement strategies, these systems can unlock new levels of decision-making and strategic planning.
11. Enterprise AI Automation and Robotic Process Automation (RPA)**: The integration of Generative AI and multimodal models into enterprise workflows is accelerating the adoption of RPA technologies. This trend is reshaping business processes by enabling AI systems to interact with GUIs, automate repetitive tasks, and enhance operational efficiency.

Cited By

• http://scholar.google.com/scholar?q=%222024%22+State+of+AI+Report+2024

Quotes

Abstract

• Artificial intelligence (AI): a broad discipline with the goal of creating intelligent machines, as opposed to the natural intelligence that is demonstrated by humans and animals.
• Artificial general intelligence (AGI): a term used to describe future machines that could match and then exceed the full range of human cognitive ability across all economically valuable tasks.
• AI Agent: an AI-powered system that can take actions in an environment. For example, an LLM that has access to a suite of tools and has to decide which one to use in order to accomplish a task that it has been prompted to do.
• AI Safety: a field that studies and attempts to mitigate the risks (minor to catastrophic) which future AI could pose to humanity.
• Computer vision (CV): the ability of a program to analyze and understand images and videos.
• Deep learning (DL): an approach to AI inspired by how neurons in the brain recognize complex patterns in data. The “deep” refers to the many layers of neurons in today’s models that help to learn rich representations of data to achieve better performance gains.
• Diffusion: an algorithm that iteratively denoises an artificially corrupted signal in order to generate new, high-quality outputs. In recent years, it has been at the forefront of image generation and protein design.
• Generative AI: a family of AI systems that are capable of generating new content (e.g. text, images, audio, or 3D assets) based on prompts.
• Graphics Processing Unit (GPU): a semiconductor processing unit that enables a large number of calculations to be computed in parallel. Historically, this was required for rendering computer graphics. Since 2012, GPUs have adapted for training DL models, which also require a large number of parallel calculations.
• Language model (LM, LLM): a model trained on vast amounts of (often) textual data to predict the next word in a self-supervised manner. The term “LLM” is used to designate multi-billion parameter LMs, but this is a moving definition.
• Machine learning (ML): a subset of AI that often uses statistical techniques to give machines the ability to "learn" from data without being explicitly given the instructions for how to do so. This process is known as “training” a “model” using a learning “algorithm” that progressively improves model performance on a specific task.
• Model: a ML algorithm trained on data and used to make predictions.
• Natural language processing (NLP): the ability of a program to understand human language as it is spoken and written.
• Prompt: a user input often written in natural language that is used to instruct an LLM to generate something or take action.
• Reinforcement learning (RL): an area of ML in which software agents learn goal-oriented behavior by trial and error in an environment that provides rewards or penalties in response to their actions (called a “policy”) towards achieving that goal.
• Self-supervised learning (SSL): a form of unsupervised learning, where manually labeled data is not needed. Raw data is instead modified in an automated way to create artificial labels to learn from. An example of SSL is learning to complete text by masking random words in a sentence and trying to predict the missing ones.
• Transformer: a model architecture at the core of most state of the art (SOTA) ML research. It is composed of multiple “attention” layers which learn which parts of the input data are the most important for a given task. Transformers started in NLP (specifically machine translation) and subsequently were expanded into computer vision, audio, and other modalities.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2024 StateofAIReport2024	Nathan Benaich Alex Chalmers			State of AI Report 2024						2024